Translating the Statistical Representation of the Effects of Education Interventions Into More Readily Interpretable Forms

By Mark Lipsey, Kelly Puzio, Cathy Yun, Michael A. Hebert, Katarzyna T. Steinka-Fry, Mike Cole, M. Roberts, Karen Anthony, Matthew Busick. 2012

478 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fa4bd8c56dc88a61f155a4db3223c61ddb46a790

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Citations in the corpus

MULTIPLE REGRESSION AS A FLEXIBLE ALTERNATIVE TO ANOVA IN L2 RESEARCH (2016, methodology): “…in Plonsky and Ziegler (2016), a mean difference effect of d ≈ .50 can be visualized using basic tools (e.g., http:// rpsychologist.com/d3/cohend/) or expressed as an alternate effect size index such as U3 to indicate the degree of nonoverlap between groups (≈70%; see Lipsey et al., 2012).”
Schema-Based Instruction for Mathematical Word Problem Solving: An Evidence-Based Review for Students With Learning Disabilities (2020, methodology): “Although Cohen (1988) established criteria for small (0.2), medium (0.5), and large (0.8) effect sizes, Lipsey et al. (2012) suggested that these effect size classifications are “. . . not tailored to the effects of intervention studies in education, much less any specific domain of education…”
Beyond small, medium, or large: points of consideration when interpreting effect sizes (2019, background): “As Lipsey et al. (2012) wrote: “The widespread indiscriminate use of Cohen’s generic small, medium, and large effect size values to characterize effect sizes in domains to which his normative values do not apply is [...] inappropriate and misleading” (p. 4).”
Falling Behind? Children’s Early Grade Retention after Paternal Incarceration (2014, background): “Our estimated effect sizes, which hover around .20, are not inconsequential, indicating a magnitude similar in size to about one-quarter of the black-white achievement gap in reading for elementary-aged children (Lipsey et al. 2012).”
Academic Interventions for Elementary and Middle School Students With Low Socioeconomic Status: A Systematic Review and Meta-Analysis (2017, methodology): “…achievement differences between high and low SES students, but neither do they significantly decrease the gap present at school start (see, e.g., Lipsey et al., 2012, for evidence of the evolution of the achievement gap in terms of the closest equivalent to the outcomes studied in this review,…”
A Meta-Analysis of Negative Feedback on Intrinsic Motivation (2018, result): “Rather than benchmarking this effect to arbitrary cut-points of small, medium, and large, we interpreted this effect in relation to other relevant studies (Lipsey et al. 2012).”
The Relationship Between Burnout, Depression, and Anxiety: A Systematic Review and Meta-Analysis (2019, background): “However, researchers have suggested that the indiscriminate use of Cohen’s generic small, medium, and large effect size values to characterize effect sizes in domains in which normative values do not apply is inappropriate and misleading (Lipsey et al., 2012).”
Synthesizing Results From Empirical Research on Computer-Based Scaffolding in STEM Education (2016, background): “One can gauge the practical importance of the results in terms of percentile gains that one would see in a given control student if computer-based scaffolding is used (Lipsey et al., 2012).”
The Effect of Teacher Coaching on Instruction and Achievement: A Meta-Analysis of the Causal Evidence (2018, background): “Effects for reading coaching programs are likely larger, in part, because of their focus on early childhood and elementary education where students make the largest learning gains (Lipsey et al., 2012).”
Interpreting effect sizes when controlling for stability effects in longitudinal autoregressive models: Implications for psychological science (2015, methodology): “Specifically, the common practice of simply citing universal guidelines for interpreting the magnitude of effect size coefficients, without considering important factors such as the type of study design, has been criticized as too rigid and potentially misleading (Lipsey et al., 2012; Peng et al., 2”
Optimal Multilevel Matching in Clustered Observational Studies: A Case Study of the Effectiveness of Private Schools Under a Large-Scale Voucher System (2014, background): “However, recent guidelines emphasize benchmarking against effect sizes from other studies on the same intervention (Lipsey et al. 2012).”
What Works, When, for Whom, and With Whom (2016, methodology): “For example, following available best practice guidelines (Lipsey et al., 2012), planners can readily use effect size data to anticipate the likelihood of an outcome given the presence of a predictor (e.”
ESG and financial performance: aggregated evidence from more than 2000 empirical studies (2015, background): “…i) and 0.169 ( p̂i) could be considered rather “small” (Cohen 1988, 1992), they reflect common effect sizes in social sciences (Richard, Bond, and Stokes-Zoota 2003; Tamim et al. 2011; Lipsey et al. 2012) and, notably, might have relatively high relevance for competitive global securities markets.”
Engaging Struggling Adolescent Readers to Improve Reading Skills (2017, background): “The effect sizes are of practical significance and suggest that STARI students showed progress across a range of components that underlie skilled reading (Lipsey et al., 2012; Scammacca et al., 2013; Slavin et al., 2008; Vaughn et al., 2013).”
Identity, Critical Agency, and Engineering: An Affective Model for Predicting Engineering as a Career Choice (2016, background): “The sizes of these effects are consistent with average effect sizes in education for broad measures such as nationally normed tests (Cohen’s d 5 0.10) from which large policy decisions are made (Lipsey et al., 2012).”